Multi-Temporal Remote Sensing Satellite Data Analysis for the 2023 Devastating Flood in Derna, Northern Libya by Roman Shults, Ashraf Farahat, Muhammad Usman, Md Masudur Rahman

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Energy-Aware Task Allocation for Multi-Cloud Networks by Sambit Kumar Mishra, Sonali Mishra, Ahmed Alsayat, N Z Jhanjhi, Mamoona Humayun, Kshira Sagar Sahoo, Ashish Kr. Luhach

“…However, the average energy consumption improved through <italic>ETAMCN</italic> is approximately 14&#x0025;, 6.3&#x0025;, and 2.8&#x0025; in opposed to the random allocation algorithm, Cloud Z-Score Normalization (<italic>CZSN</italic>) algorithm, and multi-objective scheduling algorithm with Fuzzy resource utilization (FR-MOS), respectively. …”
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Spatial and temporal distribution patterns and factors influencing hepatitis B in China: a geo-epidemiological study by Kang Fang, Na Cheng, Chuang Nie, Wentao Song, Yunkang Zhao, Jie Pan, Qi Yin, Jiwei Zheng, Qinglin Chen, Tianxin Xiang

“…Spatial autocorrelation analysis and spatiotemporal scanning were used to analyze the spatiotemporal distribution characteristics. The random forest algorithm was used to screen the potential influencing factors. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

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A Multi-Source Data-Driven Analysis of Building Functional Classification and Its Relationship with Population Distribution by Dongfeng Ren, Xin Qiu, Zehua An

“…The proposed model innovatively incorporates texture, geometric, and temporal features of building images, as well as socio-economic characteristics extracted using the distance decay algorithm. The results yield the following conclusions: (1) The proposed method achieves an overall classification accuracy of 0.77, which is 0.12 higher than that of the random forest-based approach. (2) The introduction of time features and the distance decay method further improved the model performance, increasing the accuracy by 0.04 and 0.03, respectively. (3) The correlation between the building functions and population distribution varies significantly across different scales. …”
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Cowpea genetic diversity, population structure and genome-wide association studies in Malawi: insights for breeding programs by Michael M. Chipeta, John Kafwambira, Esnart Yohane

“…The study assessed the effects of genotype, location, and their interactions on morphological traits. The Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm was used to identify significant MTAs.ResultsThe morphological traits showed significant genotype, location, and interaction effects. …”
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Neoantigen prioritization based on antigen processing and presentation by Serina Tokita, Serina Tokita, Takayuki Kanaseki, Takayuki Kanaseki, Toshihiko Torigoe

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

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Survival analysis using machine learning in transplantation: a practical introduction by Andrea Garcia-Lopez, Maritza Jiménez-Gómez, Andrea Gomez-Montero, Juan Camilo Gonzalez-Sierra, Santiago Cabas, Fernando Giron-Luque

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Analyzing Method of the Absorption Capability of New Energy in Distribution Network with Consideration of Regional Self-discipline and Absorption Quality by Yan CHEN, Wei JIN, Wenbin WANG, Huibin LI, Shengfeng HAN, Kai JIA

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UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li, Xiaoguang Liu

“…The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. …”
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Considering the effect of non-landslide sample selection on landslide susceptibility assessment by Youchen Zhu, Deliang Sun, Haijia Wen, Qiang Zhang, Qin Ji, Changming Li, Pinggen Zhou, Jianjun Zhao

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A secure medical image encryption technique based on DNA cryptography with elliptic curves by V. N. Senthil Kumaran, T. Manikandan, Rajesh Kumar Dhanaraj, Taher Al-Shehari, Nasser A. Alsadhan, Shitharth Selvarajan

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A Survey on Data Mining for Data-Driven Industrial Assets Maintenance by Eduardo Coronel, Benjamín Barán, Pedro Gardel

“…The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. …”
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A Machine Learning Approach to Evaluate the Performance of Rural Bank by Jun Wei, Tao Ye, Zhe Zhang

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Enhancing Software Defect Prediction Using Ensemble Techniques and Diverse Machine Learning Paradigms by Ayesha Siddika, Momotaz Begum, Fahmid Al Farid, Jia Uddin, Hezerul Abdul Karim

“…In supervised learning, we mainly experimented with several algorithms, including random forest, k-nearest neighbors, support vector machines, logistic regression, gradient boosting, AdaBoost classifier, quadratic discriminant analysis, Gaussian training, decision tree, passive aggressive, and ridge classifier. …”
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Target Detection Method for Soil-Dwelling Termite Damage Based on MCD-YOLOv8 by Peidong Jiang, Lai Jiang, Fengyan Wu, Tengteng Che, Ming Wang, Chuandong Zheng

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ProCAPTCHA: A profile-based CAPTCHA for personal password authentication. by Nilobon Nanglae, Pattarasinee Bhattarakosol

“…ProCAPTCHA leverages keystroke dynamics and personal information to create unique CAPTCHAs that are difficult for intruders to solve. ProCAPTCHA's algorithm generates CAPTCHA based on the user's profile data, ensuring randomness and uniqueness for each login. …”
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A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation by Mustafa Dhiaa Al-Hassani

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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. …”
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